import gradio as gr import numpy as np from PIL import Image import cv2 from insightface.app import FaceAnalysis from huggingface_hub import snapshot_download import time import subprocess import os # --- Configuration --- SECURITYLEVELS = ["128", "196", "256"] FRMODELS = ["AuraFace-v1"] EXAMPLE_IMAGES_ENROLL = ['./VGGFace2/n000001/0002_01.jpg', './VGGFace2/n000149/0002_01.jpg', './VGGFace2/n000082/0001_02.jpg', './VGGFace2/n000148/0014_01.jpg'] EXAMPLE_IMAGES_AUTH = ['./VGGFace2/n000001/0013_01.jpg', './VGGFace2/n000149/0019_01.jpg', './VGGFace2/n000082/0003_03.jpg', './VGGFace2/n000148/0043_01.jpg'] # --- Global Variables --- face_app = None DB_SUBJECT_COUNT = 1 ENROLLED_SEARCH_IMAGES = [] # --- Helper Functions --- def initialize_face_app(): """Initializes the FaceAnalysis model.""" global face_app if face_app is None: print("Initializing FaceAnalysis model...") snapshot_download("fal/AuraFace-v1", local_dir="./models/auraface") face_app = FaceAnalysis(name="auraface", providers=["CPUExecutionProvider"], root=".") face_app.prepare(ctx_id=0, det_size=(128, 128)) print("FaceAnalysis model initialized.") return face_app def run_binary(bin_path, *args): """Runs a compiled binary file and returns the result.""" if not os.path.isfile(bin_path): raise gr.Error(f"Error: Compiled binary not found at {bin_path}") command = [bin_path] + [str(arg) for arg in args] print(f"Running command: {' '.join(command)}") try: os.chmod(bin_path, 0o755) start_time = time.time() result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=True) duration = time.time() - start_time print(f"Binary execution successful. Duration: {duration:.2f}s") return result.stdout, duration except subprocess.CalledProcessError as e: print(f"Error executing binary: {e.stderr}") raise gr.Error(f"Execution failed: {e.stderr}") except Exception as e: print(f"An unexpected error occurred: {e}") raise gr.Error(f"An unexpected error occurred: {str(e)}") def extract_embedding(image_path, mode=None): """Extracts face embedding from an image path.""" if image_path is None: raise gr.Error("Please upload or select an image first.") app = initialize_face_app() try: pil_image = Image.open(image_path).convert("RGB") except Exception as e: raise gr.Error(f"Failed to open or read image file: {e}") cv2_image = np.array(pil_image) cv2_image = cv2_image[:, :, ::-1] faces = app.get(cv2_image) if not faces: raise gr.Error("No face detected. Please try another image.") embedding = faces[0].normed_embedding if mode: # For 1:1 recognition, save to the respective binary folder if mode in ["enroll", "auth"]: emb_path = f'./{mode}-emb.txt' # For 1:N search, create a subject-specific path in the search folder else: # search_enroll, search_auth if "VGGFace2" in image_path: subject = image_path.split('/')[-2] else: subject = 'uploadedSubj' os.makedirs(f'./embeddings/{subject}', exist_ok=True) emb_path = f'./embeddings/{subject}/{mode}-emb.txt' np.savetxt(emb_path, embedding.reshape(1, -1), fmt="%.6f", delimiter=',') return embedding.tolist(), emb_path return embedding.tolist() # --- UI Components --- def create_image_selection_ui(label, gallery_images): with gr.Group(): gr.HTML(f'

{label}

') image_state = gr.State() image_display = gr.Image(type="filepath", label="Selected Image", interactive=False) with gr.Tabs(): with gr.TabItem("Upload"): image_upload = gr.Image(type="filepath", label=f"Upload Image") with gr.TabItem("Select from Gallery"): image_gallery = gr.Gallery(value=gallery_images, columns=4, height="auto", object_fit="contain") # Event handlers that directly update both the hidden state and the visible display def on_select(evt: gr.SelectData): selected_image = gallery_images[evt.index] # Get the actual image path from the gallery list return selected_image, selected_image def on_upload(filepath): return filepath, filepath image_upload.change(on_upload, inputs=image_upload, outputs=[image_state, image_display]) image_gallery.select(on_select, None, outputs=[image_state, image_display]) return image_state # --- UI Styling and Theming --- css = """ @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap'); :root { --background: #EEEEEC; --background-alt: #EEEEEC; --card-bg: #FFFFFF; --card-bg-alt: rgba(255, 208, 134, 0.3); --foreground: #222; --foreground-muted: #333; --accent-orange: rgb(255, 208, 134); --accent-gradient: linear-gradient(90deg, var(--accent-orange) 0%, #333333 100%); --font-sans: 'Inter', Arial, Helvetica, sans-serif; --gray-333: #333333; } body, .gradio-container { background: var(--background); color: var(--foreground); font-family: var(--font-sans); font-size: 16px; line-height: 1.6; } .main-header { padding: 1rem; text-align: center; margin-bottom: 2rem; background: var(--gray-333); color: var(--background); border-radius: 15px; } .main-header h1 { font-size: 2.5rem; font-weight: 700; color: var(--accent-orange); margin:0; } .main-header p { font-size: 1.1rem; opacity: 0.9; margin: 0.5rem 0 0 0; } .main-header a { color: var(--background); text-decoration: none; background: transparent; padding: 0.6rem 1.5rem; border-radius: 25px; border: 1px solid var(--accent-orange); font-weight: 500; transition: all 0.3s ease; display: inline-block; margin-top: 1rem; } .main-header a:hover { background: var(--accent-orange); color: var(--gray-333); } .section-header { text-align: center; margin: 2rem 0; padding: 0 1rem; } .section-header h1 { color: var(--foreground); font-size: 2.2rem; font-weight: 600; margin-bottom: 0.5rem; } .section-header h2 { color: var(--foreground-muted); font-size: 1.5rem; font-weight: 400; margin: 0; } .narrative-section { background: var(--card-bg); border-top: 4px solid var(--accent-orange); padding: 2rem; margin: 1.5rem 0; border-radius: 12px; box-shadow: 0 4px 15px rgba(0,0,0,0.05); } .narrative-header { color: var(--foreground); margin: 0 0 1.5rem 0; font-size: 1.8rem; font-weight: 600; } .step-header { color: var(--foreground); margin: 0 0 1.5rem 0; font-size: 1.3rem; font-weight: 600; } .info-card { background: var(--card-bg-alt); border: 1px solid var(--accent-orange); border-radius: 12px; padding: 1.5rem; margin: 1.5rem 0; } .info-card h3 { color: var(--foreground); margin: 0 0 1rem 0; font-size: 1.3rem; font-weight: 600; } .info-card p { margin: 0 0 1rem 0; color: var(--foreground-muted); line-height: 1.6; } .warning-card { background: #ffebee; border: 1px solid #c62828; border-radius: 12px; padding: 1.5rem; margin: 1.5rem 0; } .warning-card h3 { color: #c62828; margin: 0 0 1rem 0; font-size: 1.3rem; font-weight: 600; } .warning-card p { margin: 0; color: #424242; line-height: 1.6; } .result-container { padding: 2rem; border-radius: 15px; text-align: center; margin-top: 1rem; color: white; } .result-container h2 { margin: 0 0 0.5rem 0; font-size: 2rem; font-weight: 600; color: white; } .result-container p { margin: 0; opacity: 0.95; font-size: 1rem; } .match-verified { background: linear-gradient(135deg, #4caf50 0%, #45a049 100%); } .no-match { background: linear-gradient(135deg, #f44336 0%, #d32f2f 100%); } .icon-lock { font-size: 4rem; margin: 1rem; } .status-text { font-size: 1.1rem; color: var(--foreground-muted); margin-top: 1rem; } """ # --- Gradio UI Definition --- with gr.Blocks(css=css) as demo: # --- Header --- gr.HTML("""

Suraksh AI

The Future of Secure Biometrics

🌐 Visit Our Website
""") # --- Key Generation on Load --- # Generate keys once for each demo when the app starts up demo.load(lambda: run_binary("./bin/genKeys.bin", "128", "genkeys"), None, None) demo.load(lambda: run_binary("./bin/search.bin", "128", "genkeys"), None, None) # --- Main Tabs for Demo Mode --- with gr.Tabs() as mode_tabs: # --- 1:1 Recognition Demo --- with gr.TabItem("πŸ‘οΈ Face Recognition (1:1)"): gr.HTML("""

Is this the same person?

A one-to-one verification demo.

""") with gr.Tabs(): # --- Vulnerable System Tab --- with gr.TabItem("🚨 The Vulnerable System"): with gr.Group(elem_classes="narrative-section"): gr.HTML('

The Problem: How Your Face Can Be Stolen

') gr.HTML("""

⚠️ Your Biometric Data is Exposed!

Most systems handle biometric data in plaintext. This means your facial embeddingβ€”a digital map of your faceβ€”can be stolen and used to reconstruct your image, creating a major privacy risk.

""") with gr.Column(): gr.HTML('

1. Original Image

') gr.Image(value=EXAMPLE_IMAGES_ENROLL[2], label="Original Face", interactive=False, show_label=False, container=False) gr.HTML('

2. Simulate Attack: Steal Data

An attacker breaches the system and steals the stored facial embedding. Click the button to simulate this theft.

') extract_btn = gr.Button("😱 Steal Biometric Data", variant="primary") with gr.Group(visible=False) as stolen_data_group: feature_output = gr.JSON(label="Stolen Feature Vector (Face Embedding)") gr.HTML('

3. Simulate Attack: Reconstruct Face

Now, the attacker uses the stolen features to create a reconstruction of the face, completely compromising the user\'s privacy.

') reconstruct_btn = gr.Button("🎭 Reconstruct Face from Stolen Data", variant="stop") with gr.Group(visible=False) as reconstructed_image_group: reconstructed_output = gr.Image(label="Reconstructed Face", interactive=False, show_label=False) def extract_and_reveal(image_path): embedding = extract_embedding(image_path) feature_json = {"embedding": embedding} return { feature_output: feature_json, stolen_data_group: gr.update(visible=True), extract_btn: gr.update(value="Data Stolen!", interactive=False) } def show_reconstruction(): reconstructed_image_path = "./static/reconstructed.png" return { reconstructed_output: reconstructed_image_path, reconstructed_image_group: gr.update(visible=True), reconstruct_btn: gr.update(interactive=False) } extract_btn.click( fn=extract_and_reveal, inputs=gr.State(EXAMPLE_IMAGES_ENROLL[0]), outputs=[feature_output, stolen_data_group, extract_btn] ) reconstruct_btn.click( fn=show_reconstruction, inputs=None, outputs=[reconstructed_output, reconstructed_image_group, reconstruct_btn] ) # --- Secure System Tab --- with gr.TabItem("βœ… The Suraksh.AI Solution"): with gr.Group(elem_classes="narrative-section"): gr.HTML('

The Solution: Verification with FHE

') gr.HTML("""

The Locked Box Analogy

With Suraksh.AI, your biometric data is encrypted inside a "locked box" before it ever leaves your device. We can perform the verification on the encrypted data without ever seeing your real face. It's mathematically impossible for us to decrypt it.

""") with gr.Row(): with gr.Column(): rec_ref_img = create_image_selection_ui("1. Provide Reference Image", EXAMPLE_IMAGES_ENROLL) with gr.Group(visible=False) as rec_ref_features_group: rec_ref_raw_features = gr.JSON(label="Raw Features (Plaintext)") rec_ref_encrypted_features = gr.Textbox(label="Encrypted Features (Ciphertext)", interactive=False, lines=5) with gr.Column(): rec_probe_img = create_image_selection_ui("2. Provide Probe Image", EXAMPLE_IMAGES_AUTH) with gr.Group(visible=False) as rec_probe_features_group: rec_probe_raw_features = gr.JSON(label="Raw Features (Plaintext)") rec_probe_encrypted_features = gr.Textbox(label="Encrypted Features (Ciphertext)", interactive=False, lines=5) with gr.Accordion("Advanced Settings", open=False): rec_threshold = gr.Slider(-512*5, 512*5, value=133, label="Match Strictness", info="A higher value means a stricter match is required.") rec_sec_level = gr.Dropdown(SECURITYLEVELS, value="128", label="Security Level") rec_run_btn = gr.Button("πŸš€ Perform Secure 1:1 Match", variant="primary", size="lg") rec_status = gr.HTML(elem_classes="status-text") rec_result = gr.HTML() def secure_recognition_flow(ref_img, probe_img, threshold, sec_level): # Reset UI yield "Initializing...", "", gr.update(visible=False), None, None, gr.update(visible=False), None, None # Process Reference Image yield "Extracting reference features...", "", gr.update(visible=False), None, None, gr.update(visible=False), None, None ref_emb, _ = extract_embedding(ref_img, "enroll") yield "Encrypting reference features...", "", gr.update(visible=True), {"embedding": ref_emb}, None, gr.update(visible=False), None, None run_binary("./bin/encReference.bin", sec_level, "encrypt") ref_ciphertext, _ = run_binary("./bin/encReference.bin", sec_level, "print") # Process Probe Image yield "βœ… Reference Encrypted. Extracting probe features...", "", gr.update(visible=True), {"embedding": ref_emb}, ref_ciphertext, gr.update(visible=False), None, None probe_emb, _ = extract_embedding(probe_img, "auth") yield "Encrypting probe features...", "", gr.update(visible=True), {"embedding": ref_emb}, ref_ciphertext, gr.update(visible=True), {"embedding": probe_emb}, None run_binary("./bin/encProbe.bin", sec_level, "encrypt") probe_ciphertext, _ = run_binary("./bin/encProbe.bin", sec_level, "print") # Perform Match yield "βœ… Probe Encrypted. Performing Secure Match...", "", gr.update(visible=True), {"embedding": ref_emb}, ref_ciphertext, gr.update(visible=True), {"embedding": probe_emb}, probe_ciphertext run_binary("./bin/recDecision.bin", sec_level, "decision", threshold) yield "βœ… Match Computed. Decrypting Result...", "", gr.update(visible=True), {"embedding": ref_emb}, ref_ciphertext, gr.update(visible=True), {"embedding": probe_emb}, probe_ciphertext output, _ = run_binary("./bin/decDecision.bin", sec_level, "decision") if output.strip().lower() == "match": result_html = f"""

βœ… MATCH VERIFIED

Identity successfully confirmed under FHE.

""" else: result_html = f"""

❌ NO MATCH

Identity verification failed.

""" yield "Done!", result_html, gr.update(visible=True), {"embedding": ref_emb}, ref_ciphertext, gr.update(visible=True), {"embedding": probe_emb}, probe_ciphertext rec_run_btn.click( fn=secure_recognition_flow, inputs=[rec_ref_img, rec_probe_img, rec_threshold, rec_sec_level], outputs=[rec_status, rec_result, rec_ref_features_group, rec_ref_raw_features, rec_ref_encrypted_features, rec_probe_features_group, rec_probe_raw_features, rec_probe_encrypted_features] ) # --- 1:N Search Demo --- with gr.TabItem("πŸ” Face Search (1:N)"): gr.HTML("""

Who is this person?

A one-to-many search demo against an encrypted database.

""") with gr.Tabs(): # --- Secure System Tab --- with gr.TabItem("βœ… The Suraksh.AI Solution"): with gr.Group(elem_classes="narrative-section"): gr.HTML('

Building and Searching a Secure Database

') gr.HTML("""

From Verification to Identification

This demo shows how FHE can be used to search for a person in a database without ever decrypting the database itself. This is ideal for large-scale, privacy-preserving identification systems.

""") with gr.Row(): with gr.Column(): gr.HTML('

1. Enroll Subjects into DB

') search_enroll_img = create_image_selection_ui("Select Image to Enroll", EXAMPLE_IMAGES_ENROLL) search_enroll_btn = gr.Button("βž• Encrypt & Add to Database", variant="secondary") with gr.Group(visible=False) as enroll_features_group: enroll_raw_features = gr.JSON(label="Raw Features (Plaintext)") enroll_encrypted_features = gr.Textbox(label="Encrypted Features (Ciphertext)", interactive=False, lines=5) search_enroll_status = gr.HTML() with gr.Column(): gr.HTML('

2. Search for a Subject

') search_probe_img = create_image_selection_ui("Select Image to Search", EXAMPLE_IMAGES_AUTH) search_run_btn = gr.Button("πŸš€ Perform Secure 1:N Search", variant="primary", size="lg") with gr.Group(visible=False) as search_features_group: search_raw_features = gr.JSON(label="Raw Features (Plaintext)") search_encrypted_features = gr.Textbox(label="Encrypted Features (Ciphertext)", interactive=False, lines=5) search_status = gr.HTML(elem_classes="status-text") search_result = gr.HTML() search_result_image = gr.Image(label="Found Reference Image", interactive=False, visible=False) with gr.Accordion("Advanced Settings", open=False): search_threshold = gr.Slider(-512*5, 512*5, value=133, label="Match Strictness") search_sec_level = gr.Dropdown(SECURITYLEVELS, value="128", label="Security Level") def secure_enroll_flow(image, sec_level): global DB_SUBJECT_COUNT, ENROLLED_SEARCH_IMAGES if image is None: raise gr.Error("Please provide an image to enroll.") current_id = DB_SUBJECT_COUNT yield "Extracting features...", gr.update(visible=False), None, None embedding, emb_path = extract_embedding(image, "search_enroll") yield "Encrypting features...", gr.update(visible=True), {"embedding": embedding}, None run_binary("./bin/search.bin", sec_level, "encRef", emb_path, current_id) ciphertext, _ = run_binary("./bin/search.bin", sec_level, "printVectorCipher", "encRef", "print") yield "Adding to secure database...", gr.update(visible=True), {"embedding": embedding}, ciphertext run_binary("./bin/search.bin", sec_level, "addRef") ENROLLED_SEARCH_IMAGES.append(image) DB_SUBJECT_COUNT += 1 yield f"βœ… Subject with ID {current_id} added. Total subjects: {DB_SUBJECT_COUNT - 1}.", gr.update(visible=True), {"embedding": embedding}, ciphertext def secure_search_flow(image, threshold, sec_level): global ENROLLED_SEARCH_IMAGES if image is None: raise gr.Error("Please provide an image to search.") yield "Extracting probe features...", "", gr.update(visible=False), None, None, gr.update(visible=False) embedding, emb_path = extract_embedding(image, "search_auth") yield "Encrypting probe features...", "", gr.update(visible=True), {"embedding": embedding}, None, gr.update(visible=False) run_binary("./bin/search.bin", sec_level, "encProbe", emb_path) ciphertext, _ = run_binary("./bin/search.bin", sec_level, "printProbe", "print") yield "βœ… Probe encrypted. Searching database...", "", gr.update(visible=True), {"embedding": embedding}, ciphertext, gr.update(visible=False) run_binary("./bin/search.bin", sec_level, "search") yield "βœ… Search complete. Decrypting results...", "", gr.update(visible=True), {"embedding": embedding}, ciphertext, gr.update(visible=False) # output, _ = run_binary("./bin/search.bin", sec_level, "decDecisionClear", threshold) output, _ = run_binary("./bin/search.bin", sec_level, "decScoreClear", threshold) print(f"Search binary output: >>>{output}<<<") lines = output.strip().split('\n') decision = lines[0].lower() if decision == "found": try: # Assuming output is "found\nID: " found_id_line = next(line for line in lines if "id:" in line.lower()) found_id = int(found_id_line.split(':')[1].strip()) # Binary ID is 1-based, list is 0-based found_image_path = ENROLLED_SEARCH_IMAGES[found_id - 1] result_html = f"""

βœ… SUBJECT FOUND

The subject was successfully found in the database with ID {found_id}.

""" result_image_update = gr.update(value=found_image_path, visible=True) except (StopIteration, IndexError, ValueError): result_html = f"""

βœ… SUBJECT FOUND

Could not parse ID from binary output: {output.strip()}

""" result_image_update = gr.update(visible=False) else: result_html = """

❌ NOT FOUND

The subject was not found in the database.

""" result_image_update = gr.update(visible=False) yield "Done!", result_html, gr.update(visible=True), {"embedding": embedding}, ciphertext, result_image_update search_enroll_btn.click( fn=secure_enroll_flow, inputs=[search_enroll_img, search_sec_level], outputs=[search_enroll_status, enroll_features_group, enroll_raw_features, enroll_encrypted_features] ) search_run_btn.click( fn=secure_search_flow, inputs=[search_probe_img, search_threshold, search_sec_level], outputs=[search_status, search_result, search_features_group, search_raw_features, search_encrypted_features, search_result_image] ) # --- Launch the Application --- if __name__ == "__main__": demo.launch()